Center for Systemic Peace

Center for International Development and Conflict Management (CIDCM): Polity IV Project

The Polity data series, originally designed by Ted Robert Gurr, contains coded annual information on regime and authority characteristics for 161 independent states (fundamentally those with greater than 500,000 total population) in the global state system and covers the years 1800-2002. The Polity database is housed at Center for International Development and Conflict Management (CIDCM), at the University of Maryland, College Park series and is available at http://www.systemicpeace.org/polity/polity4.htm (current data for Polity Project moved to this website) . Monty Marshall has been kind in providing recent updates to the IFs project.

The Polity IV dataset provides substantial information on regime types and behavior.  Polity codes were originally assigned according to three general categories of authority patterns: executive recruitment, executive constraints, and political competition. Later, these general categories were disaggregated into six component variables. IFs has drawn primarily on their indices of democracy and autocracy.

Instructions on Importing PITF data into IFs

This section explains the methodology that is to be followed when importing Political Instability Task Force (PITF) data into IFs. It also describes certain problems that a user might face in interpreting and understanding the data.

Source- PITF data is published by the center for systemic peace. The data is available at this website , http://www.systemicpeace.org/inscrdata.html

Types of Data-Following categories of data need to be imported into Ifs 

1. Ethnic War event-Describes the occurrence of Ethnic War in a region for a particular year. Data is binary, i.e. 1- War event, 0- No event
2. Ethnic War magnitude-Describes the Average death magnitude of the war event Range of 0-4. In case of multiple events in the same year for same region, select highest value amongst all available values.
3. Genocide-Politicide Event-Describes the occurrence of Genocide/Politicide event. Data is binary, i.e. 1- Event occurrence, 0-No event
4. Genocide-Politicide Magnitude- Describes the Average death magnitude of the event. Range of 0-4. In case of multiple events in the same year for same region, select highest value amongst all available values.
5. Regime Change event- Describes the occurrence of Regime Change event. Data is binary, i.e. 1- Event occurrence, 0-No event
6. Regime Change magnitude- Describes the Average death magnitude of the event Range of 0-4. In case of multiple events in the same year for same region, select highest value amongst all available values.
7. Revolutionary War event-Describes the occurrence of Revolutionary War event. Data is binary, i.e. 1- Event occurrence, 0-No event
8. Revolutionary War magnitude-Describes the Average death magnitude of the event Range of 0-4. In case of multiple events in the same year for same region, select highest value amongst all available values.

Ultimately, IFs cumulates all of the above series and computes the Internal War Event index and Internal War Magnitude Index for all countries and regions. Below is a chart describing, the Internal War Magnitude across country groups from 1955 to 2015.

Source: International Futures 7.22

Country List to be Used-Systemic Peace countries, Ifs country List (IFs Country List generally is easiest and requires least amount of changes)

Series calculated automatically by Ifs- 

There are certain series which are automatically calculated by Ifs using the above data. They have been listed here for User’s convenience,

1. SFPITFInternalWarEv
2. SFPITFInternalWarEvProb
3. SFPITFInternalWarMagAvg
4. SFPITFInternalWarY1Prob
5. SFPITFConsolidatedEv
6. SFPITFConsolidatedMag

 Issues in the data and their resolution-

1. Retrospective changes-Along with the PITF updates for future years, historical PITF data is constantly updated due to quantifications completed in this year in relation to events occurring in the previous years. E.g. The Uighur revolution that occurred in China in 2009, was added to the PITF data in 2015 since quantification was completed in 2015.  Magnitudes are also changed from time to time retrospectively. (It is highly recommended that instead of just adding current year data to the Ifs file, the User perform a comparison of historical Ifs data and the PITF data to identify any retrospective changes. This can be done by first converting the PITF data into panel format by using the pivot table function in Excel, and then using a VLOOKUP to identify differences)
2. Representation of Arab Spring- The political instability of the Arab Spring is not adequately represented in the PITF data. For example, no events exist for Tunisia in the latest PITF update. This is mainly on account of the present unavailability of data regarding the magnitude of these events. 

Instructions on Importing Polity IV Data into IFs

This section explains the methodology that is to be followed when importing Polity IV data into IFs. It also describes certain problems that a user might face in interpreting and understanding the data.

Source: The data for Polity IV is hosted by the Center for Systemic Peace and can be found at http://www.systemicpeace.org/inscrdata.html

Series to be Imported: There are ten series from Polity IV that need to be imported into IFs:

1. PolityAutoc: This is the autocracy score from Polity IV. It is a scale from 1-10, with negative values that indicate transitional periods (these negative numbers should be removed when importing into IFs).
2. PolityDemoc: This is the democracy score from Polity IV. It is a scale from 1-10, with negative values that indicate transitional periods (these negative numbers should be removed when importing into IFs).
3. PolityCombined: This is the combined score from Polity IV, It is a scale from -10 to 10 (democracy score minus autocracy score), with lower negative values that indicate transitional periods (these negative numbers should be removed when importing into IFs). For the purposes of importing into IFs, this series should be transformed into a 0-20 scale, simply by adding 10 to the score.
4.  PolityDurable: This is the regime durability measure, and it is the number of years since a regime change, with negative values that indicate transitional periods (these negative numbers should be removed when importing into IFs).
5. PolityExecConstrain: This is a categorical variable measuring the constraints on executive power. It is a scale from 1-7, with negative values indicating transitional periods (these negative numbers whould be removed when importing into IFs). 
6. PolityExecRecruitOpen: This is a categorical variable measuring the openness of executive recruitment. It is a scale from 1-4, with negative numbers indicating transitional periods (these negative numbers should be removed when importing into IFs). 
7. PolityExecRecruitComp: This is a categorical variable measuring how competitive executive recruitment is in a country. It is a scale from 1-3, with negative numbers indicating transitional periods (these negative numbers should be removed when importing into IFs). 
8. PolityExecRecruitRegu: This is a categorical variable measuring the regulation of executive recruitment in a country. It is a scale from 1-3, with negative numbers indicating transitional periods (these negative numbers should be removed when importing into IFs). 
9. PolityParticCompet: This is a categorical variable measuring the competitiveness of political participation. It is a scale from 0-5, with negative numbers indicating transitional periods (these negative numbers should be removed when importing into IFs). 
10. PolityParticRegulate: This is a categorical variable measuring the regulation of political participation in a country. It is a scale from 1-5, with negative numbers indicating transitional periods (these negative numbers should be removed when importing into IFs). 
11. PolityPartialAutocCat:
12. PolityPartialAutocCat2:
13. PolityPartialDemocCat:
14. PolityPartialDemocCat2:

Country Translation: Use the Polity Countries list

Issues in the Data and Their Solutions

1. Historical Data: There is historical data for Yugoslavia, Serbia and Montenegro, the USSR, Korea, and Czechoslovakia. These countries map to their modern counterparts, Serbia, Russia, South Korea, and the Czech Republic, respectively. The historical data for countries that were split into north and south and then unified does not map to any IFs countries.  
2. Country Concordance: The Polity IV data has multiple three letter codes for the same countries, and the concordance table needs to be adjusted to account for that. Czech Republic maps to both CZR and CZE. Ethiopia maps to both ETH and ETI. Russian Federation maps to both RUS and USR. Sudan maps to both SUD and SDN. Serbia maps to SER, YUG, and YGS, Pakistan maps to both PAK and PKS, Korea maps to KOR and ROK.
3. Issues with pre-processor- For Autocracy and Democracy, Ifs does not use any data for the year 2013 when building the forecasts for the polity score. This, in spite of rebuilding the Base and Historical Base after the data update.